# OpenSearch-VL: An Open Recipe for Frontier Multimodal Search Agents

arXiv: https://arxiv.org/abs/2605.05185 · HTML: https://mlsanity.com/paper/2605.05185/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 49 references that are in the map.
- First posted on arXiv 6 May 2026.

## Builds on (most cited first, 30 of 41)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning](https://mlsanity.com/paper/2503.09516/) (2025)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [OpenAI GPT-5 System Card](https://mlsanity.com/paper/2601.03267/) (2025)
- [Video-R1: Reinforcing Video Reasoning in MLLMs](https://mlsanity.com/paper/2503.21776/) (2025)
- [VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents](https://mlsanity.com/paper/2410.10594/) (2024)
- [WebSailor: Navigating Super-human Reasoning for Web Agent](https://mlsanity.com/paper/2507.02592/) (2025)
- [Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?](https://mlsanity.com/paper/2302.11713/) (2023)
- [WebDancer: Towards Autonomous Information Seeking Agency](https://mlsanity.com/paper/2505.22648/) (2025)
- [FVQA: Fact-based Visual Question Answering](https://mlsanity.com/paper/1606.05433/) (2016)
- [DeepEyesV2: Toward Agentic Multimodal Model](https://mlsanity.com/paper/2511.05271/) (2025)
- [MMSearch-R1: Incentivizing LMMs to Search](https://mlsanity.com/paper/2506.20670/) (2025)
- [WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent](https://mlsanity.com/paper/2508.05748/) (2025)
- [SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Models](https://mlsanity.com/paper/2502.13059/) (2025)
- [OneThinker: All-in-one Reasoning Model for Image and Video](https://mlsanity.com/paper/2512.03043/) (2025)
- [Visual Agentic Reinforcement Fine-Tuning](https://mlsanity.com/paper/2505.14246/) (2025)
- [Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning](https://mlsanity.com/paper/2506.04207/) (2025)
- [DeepMMSearch-R1: Empowering Multimodal LLMs in Multimodal Web Search](https://mlsanity.com/paper/2510.12801/) (2025)
- [Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models](https://mlsanity.com/paper/2601.22060/) (2026)
- [Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models](https://mlsanity.com/paper/2602.02185/) (2026)
- [SenseNova-MARS: Empowering Multimodal Agentic Reasoning and Search via Reinforcement Learning](https://mlsanity.com/paper/2512.24330/) (2025)
- [Gen-Searcher: Reinforcing Agentic Search for Image Generation](https://mlsanity.com/paper/2603.28767/) (2026)
- [Skywork-R1V4: Toward Agentic Multimodal Intelligence through Interleaved Thinking with Images and DeepResearch](https://mlsanity.com/paper/2512.02395/) (2025)

## Built on by (most cited first, 21 of 21)

- [Towards On-Policy Data Evolution for Visual-Native Multimodal Deep Search Agents](https://mlsanity.com/paper/2605.10832/) (2026)
- [Visual-Seeker: Towards Visual-Native Multimodal Agentic Search via Active Visual Reasoning](https://mlsanity.com/paper/2606.15231/) (2026)
- [PBSD: Privileged Bayesian Self-Distillation for Long-Horizon Credit Assignment](https://mlsanity.com/paper/2606.09348/) (2026)
- [SearchEyes: Towards Frontier Multimodal Deep Search Intelligence via Search World Simulation](https://mlsanity.com/paper/2607.05943/) (2026)
- [From Web to Pixels: Bringing Agentic Search into Visual Perception](https://mlsanity.com/paper/2605.12497/) (2026)
- [VistaHop: Benchmarking Long-Horizon Visual DeepSearch](https://mlsanity.com/paper/2606.03273/) (2026)
- [SimpleSearch-VL: A Simple Recipe for Multimodal Agentic Deep Search](https://mlsanity.com/paper/2606.31504/) (2026)
- [DeepVoyager-VL: Incentivizing Vision-in-the-Loop Search for Long-Horizon Multimodal Agents](https://mlsanity.com/paper/2608.01827/) (2026)
- [Video-DeepResearch: Towards the Next-Generation Multimodal Deepresearch Agent](https://mlsanity.com/paper/2608.03979/) (2026)
- [VibeWorlding: Can Multimodal Agents Construct 3D Open Worlds End-to-End?](https://mlsanity.com/paper/2608.15265/) (2026)
- [WeAgent-MMSearch: Native Text-Vision Interaction for Multimodal Search Agents](https://mlsanity.com/paper/2608.28062/) (2026)
- [VideoGAIA: A Benchmark for General AI Assistants on Agentic Video Understanding](https://mlsanity.com/paper/2608.14718/) (2026)
- [VisDocAgentBench: Benchmarking Agents for Visually Rich Document Retrieval](https://mlsanity.com/paper/2608.17889/) (2026)
- [Thinking Beyond Videos: Unifying Video Reasoning and Deep Research for Open-World Video Agents](https://mlsanity.com/paper/2608.23329/) (2026)
- [AdaVDR: Adaptive Tool Use and Reflection for Video Deep Research](https://mlsanity.com/paper/2608.25559/) (2026)
- [LiteSearch-VL: Small Multimodal Search Agents via Trajectory Distillation and Synthetic Step-DPO](https://mlsanity.com/paper/2608.29357/) (2026)
- [Stable-MM-R1: Anchoring Multimodal Reasoning Dynamics via Entropy-Guided Stratification](https://mlsanity.com/paper/2609.07148/) (2026)
- [Long-MDR: Long-Context Reinforcement Learning for Multimodal Deep-Research Agents](https://mlsanity.com/paper/2610.05195/) (2026)
- [OneSearch-VL: Unified Multimodal Deep Research Agent for Image and Video](https://mlsanity.com/paper/2610.12419/) (2026)
- [Neos: Learning to Interleave Search and Visual Reasoning](https://mlsanity.com/paper/2610.17043/) (2026)
- [ActICL: Post-Training for Agentic Multimodal In-Context Learning](https://mlsanity.com/paper/2610.19475/) (2026)

## Abstract (arXiv)

Deep search has become a crucial capability for frontier multimodal agents, enabling models to solve complex questions through active search, evidence verification, and multi-step reasoning. Despite rapid progress, top-tier multimodal search agents remain difficult to reproduce, largely due to the absence of open high-quality training data, transparent trajectory synthesis pipelines, or detailed training recipes. To this end, we introduce OpenSearch-VL, a fully open-source recipe for training frontier multimodal deep search agents with agentic reinforcement learning. First, we curated a dedicated pipeline to construct high-quality training data through Wikipedia path sampling, fuzzy entity rewriting, and source-anchor visual grounding, which jointly reduce shortcuts and one-step retrieval collapse. Based on this pipeline, we curate two training datasets, SearchVL-SFT-36k for SFT and SearchVL-RL-8k for RL. Besides, we design a diverse tool environment that unifies text search, image search, OCR, cropping, sharpening, super-resolution, and perspective correction, enabling agents to combine active perception with external knowledge acquisition. Finally, we propose a multi-turn fatal-aware GRPO training algorithm that handles cascading tool failures by masking post-failure tokens while preserving useful pre-failure reasoning through one-sided advantage clamping. Built on this recipe, OpenSearch-VL delivers substantial performance gains, with over 10-point average improvements across seven benchmarks, and achieves results comparable to proprietary commercial models on several tasks. We will release all data, code, and models to support open research on multimodal deep search agents.